Text Generation
GGUF
Japanese
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llama.cpp
Mixture of Experts
expert-pruning
intel-mac
cpu
local-agent
imatrix
conversational
Instructions to use miutti/intel-mac-local-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use miutti/intel-mac-local-llm with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- LM Studio
- Jan
- vLLM
How to use miutti/intel-mac-local-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miutti/intel-mac-local-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miutti/intel-mac-local-llm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Ollama
How to use miutti/intel-mac-local-llm with Ollama:
ollama run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Unsloth Desktop
- Pi
How to use miutti/intel-mac-local-llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "miutti/intel-mac-local-llm:UD-Q2_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use miutti/intel-mac-local-llm with Docker Model Runner:
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Lemonade
How to use miutti/intel-mac-local-llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miutti/intel-mac-local-llm:UD-Q2_K_XL
Run and chat with the model
lemonade run user.intel-mac-local-llm-UD-Q2_K_XL
List all available models
lemonade list
- Hermes Agent
How to use miutti/intel-mac-local-llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default miutti/intel-mac-local-llm:UD-Q2_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use miutti/intel-mac-local-llm with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "miutti/intel-mac-local-llm:UD-Q2_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 10,597 Bytes
df41178 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""agent.py -- 手元モデルと、読み取り専用の道具をつなぐ小さな実行ループ。
書き込み・シェル実行・アプリ操作はここでは扱わない。モデルが道具を
呼んでも、見られる場所は Desktop / Downloads / Documents / このアプリの
記録置き場に限る。変更が必要な依頼は、既存の kernel.py の確認付き経路へ
戻すための材料だけを返す。
"""
from __future__ import annotations
import datetime as _dt
import fnmatch
import json
import os
import time
import urllib.request
_HOME = os.path.expanduser("~")
_HERE = os.path.dirname(os.path.abspath(__file__))
_ROOTS = tuple(os.path.realpath(os.path.join(_HOME, p)) for p in (
"Desktop", "Downloads", "Documents",
os.path.join("Library", "Application Support", "kernel-ai"),
)) + (os.path.realpath(_HERE),)
_ALIASES = {
"Desktop": os.path.join(_HOME, "Desktop"),
"デスクトップ": os.path.join(_HOME, "Desktop"),
"Downloads": os.path.join(_HOME, "Downloads"),
"ダウンロード": os.path.join(_HOME, "Downloads"),
"Documents": os.path.join(_HOME, "Documents"),
"書類": os.path.join(_HOME, "Documents"),
}
def _safe_path(path: str, want_dir: bool | None = None) -> str:
if not isinstance(path, str) or not path.strip() or "\x00" in path:
raise ValueError("場所が空です")
raw = _ALIASES.get(path.strip(), path.strip())
if not os.path.isabs(raw):
raise ValueError("絶対パスか Desktop / Downloads / Documents を指定してください")
ap = os.path.realpath(os.path.expanduser(raw))
if not any(ap == root or ap.startswith(root + os.sep) for root in _ROOTS):
raise PermissionError("許可された場所の外です")
if not os.path.exists(ap):
raise FileNotFoundError(ap)
if want_dir is True and not os.path.isdir(ap):
raise NotADirectoryError(ap)
if want_dir is False and not os.path.isfile(ap):
raise IsADirectoryError(ap)
return ap
def _json(value) -> str:
return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
def get_current_time(_args: dict) -> str:
return _json({"日時": _dt.datetime.now().astimezone().isoformat(),
"曜日": "月火水木金土日"[_dt.datetime.now().weekday()]})
def list_directory(args: dict) -> str:
path = _safe_path(args.get("path", "Desktop"), want_dir=True)
pattern = args.get("pattern", "")
if not isinstance(pattern, str):
pattern = ""
try:
limit = max(1, min(100, int(args.get("limit", 40))))
except (TypeError, ValueError):
limit = 40
rows = []
with os.scandir(path) as it:
for ent in sorted(it, key=lambda e: e.name.casefold()):
if pattern and not fnmatch.fnmatch(ent.name, pattern):
continue
try:
st = ent.stat(follow_symlinks=False)
rows.append({"名前": ent.name,
"種類": "フォルダ" if ent.is_dir(follow_symlinks=False) else "ファイル",
"バイト": st.st_size,
"更新": _dt.datetime.fromtimestamp(st.st_mtime).astimezone().isoformat()})
except OSError:
continue
if len(rows) >= limit:
break
return _json({"場所": path, "件数": len(rows), "一覧": rows})
def read_text_file(args: dict) -> str:
path = _safe_path(args.get("path", ""), want_dir=False)
try:
limit = max(200, min(20000, int(args.get("max_chars", 12000))))
except (TypeError, ValueError):
limit = 12000
if os.path.getsize(path) > 2_000_000:
raise ValueError("大きすぎるファイルです(2MB以下だけ読めます)")
with open(path, "rb") as f:
raw = f.read(limit * 4 + 1)
text = raw.decode("utf-8", "replace")
clipped = len(text) > limit
return _json({"パス": path, "内容": text[:limit], "省略": clipped})
def find_files(args: dict) -> str:
root = _safe_path(args.get("root", "Desktop"), want_dir=True)
pattern = args.get("pattern", "*")
if not isinstance(pattern, str) or not pattern or len(pattern) > 120:
raise ValueError("検索パターンが不正です")
try:
limit = max(1, min(100, int(args.get("limit", 40))))
except (TypeError, ValueError):
limit = 40
rows = []
for base, dirs, files in os.walk(root, followlinks=False):
dirs[:] = [d for d in dirs if not d.startswith(".")]
for name in files:
if fnmatch.fnmatch(name, pattern):
p = os.path.join(base, name)
try:
rows.append({"名前": name, "パス": p, "バイト": os.path.getsize(p)})
except OSError:
pass
if len(rows) >= limit:
return _json({"場所": root, "件数": len(rows), "一覧": rows,
"省略": True})
return _json({"場所": root, "件数": len(rows), "一覧": rows, "省略": False})
TOOLS = [
{"type": "function", "function": {
"name": "get_current_time", "description": "現在の日時を返す。",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "list_directory", "description": "許可されたフォルダの中身を一覧する。",
"parameters": {"type": "object", "properties": {
"path": {"type": "string", "description": "Desktop / Downloads / Documents または許可範囲の絶対パス"},
"pattern": {"type": "string", "description": "任意のファイル名パターン。例: *.pdf"},
"limit": {"type": "integer", "minimum": 1, "maximum": 100}},
"required": ["path"]}}},
{"type": "function", "function": {
"name": "read_text_file", "description": "許可された範囲の小さなUTF-8テキストを読む。",
"parameters": {"type": "object", "properties": {
"path": {"type": "string"},
"max_chars": {"type": "integer", "minimum": 200, "maximum": 20000}},
"required": ["path"]}}},
{"type": "function", "function": {
"name": "find_files", "description": "許可されたフォルダ以下からファイル名を探す。",
"parameters": {"type": "object", "properties": {
"root": {"type": "string"},
"pattern": {"type": "string"},
"limit": {"type": "integer", "minimum": 1, "maximum": 100}},
"required": ["root", "pattern"]}}},
]
_FUNCS = {"get_current_time": get_current_time,
"list_directory": list_directory,
"read_text_file": read_text_file,
"find_files": find_files}
def _call(url: str, payload: dict, timeout: int) -> dict:
req = urllib.request.Request(url.rstrip("/") + "/v1/chat/completions",
data=json.dumps(payload, ensure_ascii=False).encode(),
headers={"Content-Type": "application/json"})
with urllib.request.urlopen(req, timeout=timeout) as f:
return json.loads(f.read().decode("utf-8"))
def run(text: str, url: str, model: str = "qwen3.5-35b", max_steps: int = 3,
timeout: int = 240) -> dict:
"""読み取り専用ツールを最大 max_steps 回だけ実行して答える。"""
t0 = time.monotonic()
messages = [
{"role": "system", "content": (
"あなたはこのMacの読み取り専用アシスタントです。"
"必要なら提供された道具を呼び、結果にないことは推測しないでください。"
"書き込み・削除・実行・送信はできません。日本語で簡潔に答えてください。")},
{"role": "user", "content": text},
]
trace = []
for step in range(max(1, min(4, int(max_steps)))):
left = max(10, int(timeout - (time.monotonic() - t0)))
try:
body = _call(url, {"model": model, "messages": messages,
"tools": TOOLS, "tool_choice": "auto",
"temperature": 0, "max_tokens": 384,
"stream": False,
"chat_template_kwargs": {"enable_thinking": False}}, left)
except Exception as e:
return {"text": "", "error": "%s: %s" % (type(e).__name__, e),
"steps": step, "tools": trace,
"ms": int((time.monotonic() - t0) * 1000)}
choices = body.get("choices") or []
if not choices:
return {"text": "", "error": "モデルから選択肢が返りませんでした",
"steps": step + 1, "tools": trace,
"ms": int((time.monotonic() - t0) * 1000)}
msg = choices[0].get("message") or {}
calls = msg.get("tool_calls") or []
if not calls:
answer = (msg.get("content") or "").strip()
return {"text": answer, "error": None if answer else "空応答",
"steps": step + 1, "tools": trace,
"ms": int((time.monotonic() - t0) * 1000)}
assistant = {"role": "assistant", "content": msg.get("content") or "",
"tool_calls": calls}
messages.append(assistant)
for call in calls[:4]:
fn = call.get("function") or {}
name = fn.get("name") or ""
raw = fn.get("arguments") or "{}"
try:
args = json.loads(raw) if isinstance(raw, str) else raw
if not isinstance(args, dict):
raise ValueError("引数はJSONオブジェクトで指定してください")
if name not in _FUNCS:
raise ValueError("許可されていない道具です")
result = _FUNCS[name](args)
ok = True
except Exception as e:
result = _json({"error": str(e)})
ok = False
call_id = call.get("id") or ("tool-%d" % len(trace))
messages.append({"role": "tool", "tool_call_id": call_id,
"content": result})
trace.append({"name": name, "ok": ok})
return {"text": "", "error": "道具の呼び出し回数が上限に達しました",
"steps": max_steps, "tools": trace,
"ms": int((time.monotonic() - t0) * 1000)}
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